An Emotion-controlled Dialog Response Generation Model with Dynamic Vocabulary
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Abstract (arXiv)
In response generation task, proper sentimental expressions can obviously improve the human-like level of the responses. However, for real application in online systems, high QPS (queries per second, an indicator of the flow capacity of on-line systems) is required, and a dynamic vocabulary mechanism has been proved available in improving speed of generative models. In this paper, we proposed an emotion-controlled dialog response generation model based on the dynamic vocabulary mechanism, and the experimental results show the benefit of this model.
Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Builds on 7 of its 8 references that are in the map. First posted on arXiv 4 Mar 2021.
Builds on
7 of 8 references.
References · 7
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,024
- 2016How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generationcited by 356
- 2017Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoderscited by 222
- 2015A Neural Conversational Modelcited by 196
- 2018From Eliza to XiaoIce: Challenges and Opportunities with Social Chatbotscited by 85
- 2018Joint Embedding of Words and Labels for Text Classificationcited by 69
- 2017Neural Response Generation with Dynamic Vocabulariescited by 8
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2 papers.